Wind turbine blade failure can be catastrophic and lead to unexpected power interruptions. In this paper, a Structural Health Monitoring (SHM) algorithm is presented for wireless monitoring of wind turbine blades. The SHM algorithm utilizes accumulated strain energy data, such as would be acquired by piezoelectric materials. The SHM algorithm compares the accumulated strain energy at the same position on the three blades. This exploits the inherent triple redundancy of the blades and avoids the need for a structural model of the blade. The performance of the algorithm is evaluated using probabilistic metrics such as detection probability (True Positive) and false alarm rate (False Positive). The decision time is chosen to be sufficiently long that a particular damage level can be detected even in the presence of system sensor noise and wind variations. Finally, the proposed algorithm is evaluated with a case study of a utility-scale turbine. The noise level is based on measurements acquired from strain sensors mounted on the blades of a Clipper Liberty C96 turbine. Strain energy changes associated with damage from matrix cracking and delamination are simulated with a finite element model. The case study demonstrates that the proposed algorithm can detect damage with a high probability based on a decision time period of approximately 50–200 days.
Bibliographical noteFunding Information:
This work was supported by the University of Minnesota Institute on the Environment, IREE Grant No. RS-0029-12 entitled ?Development of self-powered wireless sensor for structural health monitoring in wind turbine blades.? This work was also partially supported by the National Science Foundation under Grant No. NSF-CMMI-1254129 entitled ?CAREER: Probabilistic Tools for High Reliability Monitoring and Control of Wind Farms.? Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF.
Copyright © 2016 John Wiley & Sons, Ltd.
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- composite material failure
- model-free algorithm
- piezoelectric wireless sensor
- probabilistic analysis
- structural health monitoring
- wind turbine blade